Quantitative structure-activity (QSAR) relationships of mutagenic aromatic and heterocyclic amines

In Silico Prediction of Food Properties: A Multiscale Perspective

Several open software packages have popularized modeling and simulation strategies at the food product scale. Food processing and key digestion steps can be described in 3D using the principles of continuum mechanics. However, compared to other branches of engineering, the necessary transport, mechanical, chemical, and thermodynamic properties have been insufficiently tabulated and documented. Natural variability, accented by food evolution during processing and deconstruction, requires considering composition and structure-dependent properties. This review presents practical approaches where the premises for modeling and simulation start at a so-called “microscopic” scale where constituents or phase properties are known. The concept of microscopic or ground scale is shown to be very flexible from atoms to cellular structures. Zooming in on spatial details tends to increase the overall cost of simulations and the integration over food regions or time scales. The independence of scales facilitates the reuse of calculations and makes multiscale modeling capable of meeting food manufacturing needs. On one hand, new image-modeling strategies without equations or meshes are emerging. On the other hand, complex notions such as compositional effects, multiphase organization, and non-equilibrium thermodynamics are naturally incorporated in models without linearization or simplifications. Multiscale method’s applicability to hierarchically predict food properties is discussed with comprehensive examples relevant to food science, engineering and packaging. Entropy-driven properties such as transport and sorption are emphasized to illustrate how microscopic details bring new degrees of freedom to explore food-specific concepts such as safety, bioavailability, shelf-life and food formulation. Routes for performing spatial and temporal homogenization with and without chemical details are developed. Creating a community sharing computational codes, force fields, and generic food structures is the next step and should be encouraged. This paper provides a framework for the transfer of results from other fields and the development of methods specific to the food domain.

Mechanism-Based Insights into Removing the Mutagenicity of Aromatic Amines by Small Structural Alterations

Aromatic and heteroaromatic amines (ArNH₂) are activated by cytochrome P450 monooxygenases, primarily CYP1A2, into reactive N-arylhydroxylamines that can lead to covalent adducts with DNA nucleobases. Hereby, we give hands-on mechanism-based guidelines to design mutagenicity-free ArNH₂. The mechanism of N-hydroxylation of ArNH₂ by CYP1A2 is investigated by density functional theory (DFT) calculations. Two putative pathways are considered, the radicaloid route that goes via the classical ferryl-oxo oxidant and an alternative anionic pathway through Fenton-like oxidation by ferriheme-bound H₂O₂. Results suggest that bioactivation of ArNH₂ follows the anionic pathway. We demonstrate that H-bonding and/or geometric fit of ArNH₂ to CYP1A2 as well as feasibility of both proton abstraction by the ferriheme-peroxo base and heterolytic cleavage of arylhydroxylamines render molecules mutagenic. Mutagenicity of ArNH₂ can be removed by structural alterations that disrupt geometric and/or electrostatic fit to CYP1A2, decrease the acidity of the NH₂ group, destabilize arylnitrenium ions, or disrupt their pre-covalent transition states with guanine.

Mechanism-based structure-activity relationship (SAR) analysis of aromatic amines and nitroaromatics carcinogenicity via statistical analyses based on CPDB

Cancer is a leading cause of human mortality around the globe. In this study, mechanism-based SAR (Structure-Activity Relationship) was employed to investigate the carcinogenicity of aromatic amines and nitroaromatics based on CPDB. Principal component analysis and cluster analysis were used to construct the SAR model. Principle component analysis generated three principal components from 12 mechanism-based descriptors. The extracted principal components were later used for cluster analysis, which divided the selected 55 chemicals into six clusters. The three principal components were proposed to describe the "transport", "reactivity" and "electrophilicity" properties of the chemicals. Cluster analysis indicated that the relevant "transport" properties positively correlated with the carcinogenic potential and were contributing factors in determining the carcinogenicity of the studied chemicals. The mechanism-based SAR analysis suggested the electron donating groups, electron withdrawing groups and planarity are significant factors in determining the carcinogenic potency for studied aromatic compounds. The present study may provide insights into the relationship between the three proposed properties and the carcinogenesis of aromatic amines and nitroaromatics.

Computational identification of structural factors affecting the mutagenic potential of aromatic amines: study design and experimental validation

A grid-based, alignment-independent 3D-SDAR (three-dimensional spectral data-activity relationship) approach based on simulated ¹³C and ¹⁵N NMR chemical shifts augmented with through-space interatomic distances was used to model the mutagenicity of 554 primary and 419 secondary aromatic amines. A robust modeling strategy supported by extensive validation including randomized training/hold-out test set pairs, validation sets, "blind" external test sets as well as experimental validation was applied to avoid over-parameterization and build Organization for Economic Cooperation and Development (OECD 2004) compliant models. Based on an experimental validation set of 23 chemicals tested in a two-strain Salmonella typhimurium Ames assay, 3D-SDAR was able to achieve performance comparable to 5-strain (Ames) predictions by Lhasa Limited's Derek and Sarah Nexus for the same set. Furthermore, mapping of the most frequently occurring bins on the primary and secondary aromatic amine structures allowed the identification of molecular features that were associated either positively or negatively with mutagenicity. Prominent structural features found to enhance the mutagenic potential included: nitrobenzene moieties, conjugated π-systems, nitrothiophene groups, and aromatic hydroxylamine moieties. 3D-SDAR was also able to capture "true" negative contributions that are particularly difficult to detect through alternative methods. These include sulphonamide, acetamide, and other functional groups, which not only lack contributions to the overall mutagenic potential, but are known to actively lower it, if present in the chemical structures of what otherwise would be potential mutagens.

QSAR modeling for predicting mutagenic toxicity of diverse chemicals for regulatory purposes

The safety assessment process of chemicals requires information on their mutagenic potential. The experimental determination of mutagenicity of a large number of chemicals is tedious and time and cost intensive, thus compelling for alternative methods. We have established local and global QSAR models for discriminating low and high mutagenic compounds and predicting their mutagenic activity in a quantitative manner in Salmonella typhimurium (TA) bacterial strains (TA98 and TA100). The decision treeboost (DTB)-based classification QSAR models discriminated among two categories with accuracies of >96% and the regression QSAR models precisely predicted the mutagenic activity of diverse chemicals yielding high correlations (R ²) between the experimental and model-predicted values in the respective training (>0.96) and test (>0.94) sets. The test set root mean squared error (RMSE) and mean absolute error (MAE) values emphasized the usefulness of the developed models for predicting new compounds. Relevant structural features of diverse chemicals that were responsible and influence the mutagenic activity were identified. The applicability domains of the developed models were defined. The developed models can be used as tools for screening new chemicals for their mutagenicity assessment for regulatory purpose.

A knowledge-based expert rule system for predicting mutagenicity (Ames test) of aromatic amines and azo compounds

Cancer is one of the main causes of death in Western countries, and a major issue for human health. Prolonged exposure to a number of chemicals was observed to be one of the primary causes of cancer in occupationally exposed persons. Thus, the development of tools for identifying hazardous chemicals and the increase of mechanistic understanding of their toxicity is a major goal for scientific research. We constructed a new knowledge-based expert system accounting the effect of different substituents for the prediction of mutagenicity (Ames test) of aromatic amines, a class of compounds of major concern because of their widespread application in industry. The herein presented model implements a series of user-defined structural rules extracted from a database of 616 primary aromatic amines, with their Ames test outcomes, aimed at identifying mutagenic and non-mutagenic chemicals. The chemical rationale behind such rules is discussed. Besides assessing the model's ability to correctly classify aromatic amines, its predictivity was further evaluated on a second database of 354 azo dyes, another class of chemicals of major concern, whose toxicity has been predicted on the basis of the toxicity of aromatic amines potentially generated from the metabolic reduction of the azo bond. Good performance in classification on both the amine (MCC, Matthews Correlation Coefficient=0.743) and the azo dye (MCC=0.584) datasets confirmed the predictive power of the model, and its suitability for use on a wide range of chemicals. Finally, the model was compared with a series of well-known mutagenicity predicting software. The good performance of our model compared with other mutagenicity models, especially in predicting azo dyes, confirmed the usefulness of this expert system as a reliable support to in vitro mutagenicity assays for screening and prioritization purposes. The model has been fully implemented as a KNIME workflow and is freely available for downstream users.

An investigation into pharmaceutically relevant mutagenicity data and the influence on Ames predictive potential

Background

In drug discovery, a positive Ames test for bacterial mutation presents a significant hurdle to advancing a drug to clinical trials. In a previous paper, we discussed success in predicting the genotoxicity of reagent-sized aryl-amines (ArNH₂), a structure frequently found in marketed drugs and in drug discovery, using quantum mechanics calculations of the energy required to generate the DNA-reactive nitrenium intermediate (ArNH:+). In this paper we approach the question of what molecular descriptors could improve these predictions and whether external data sets are appropriate for further training.

Results

In trying to extend and improve this model beyond this quantum mechanical reaction energy, we faced considerable difficulty, which was surprising considering the long history and success of QSAR model development for this test. Other quantum mechanics descriptors were compared to this reaction energy including AM1 semi-empirical orbital energies, nitrenium formation with alternative leaving groups, nitrenium charge, and aryl-amine anion formation energy. Nitrenium formation energy, regardless of the starting species, was found to be the most useful single descriptor. External sets used in other QSAR investigations did not present the same difficulty using the same methods and descriptors. When considering all substructures rather than just aryl-amines, we also noted a significantly lower performance for the Novartis set. The performance gap between Novartis and external sets persists across different descriptors and learning methods. The profiles of the Novartis and external data are significantly different both in aryl-amines and considering all substructures. The Novartis and external data sets are easily separated in an unsupervised clustering using chemical fingerprints. The chemical differences are discussed and visualized using Kohonen Self-Organizing Maps trained on chemical fingerprints, mutagenic substructure prevalence, and molecular weight.

Conclusions

Despite extensive work in the area of predicting this particular toxicity, work in designing and publishing more relevant test sets for compounds relevant to drug discovery is still necessary. This work also shows that great care must be taken in using QSAR models to replace experimental evidence. When considering all substructures, a random forest model, which can inherently cover distinct neighborhoods, built on Novartis data and previously reported external data provided a suitable model.

Avoidance of the Ames test liability for aryl-amines via computation

Aryl-amines are commonly used synthons in modern drug discovery, however a minority of these chemical templates have the potential to cause toxicity through mutagenicity. The toxicity mostly arises through a series of metabolic steps leading to a reactive electrophilic nitrenium cation intermediate that reacts with DNA nucleotides causing mutation. Highly detailed in silico calculations of the energetics of chemical reactions involved in the metabolic formation of nitrenium cations have been performed. This allowed a critical assessment of the accuracy and reliability of using a theoretical formation energy of the DNA-reactive nitrenium intermediate to correlate with the Ames test response. This study contains the largest data set reported to date, and presents the in silico calculations versus the in vitro Ames response data in the form of beanplots commonly used in statistical analysis. A comparison of this quantum mechanical approach to QSAR and knowledge-based methods is also reported, as well as the calculated formation energies of nitrenium ions for thousands of commercially available aryl-amines generated as a watch-list for medicinal chemists in their synthetic optimization strategies.

Evaluation of the OECD QSAR Application Toolbox and Toxtree for estimating the mutagenicity of chemicals. Part 1. Aromatic amines

The Ames Salmonella typhimurium mutagenicity assay is a short-term bacterial reverse mutation test that was designed to detect mutagens. For several decades, it has been used in research laboratories and by regulatory agencies throughout the world for the detection and characterization of potential mutagens among natural products and man-made chemicals. Faced with the ever-growing number of chemicals available on the market, congeneric and non-congeneric (Q)SAR models have been designed from Ames test results obtained on specific S. typhimurium strains such as TA 100 or TA 98. Such models have great potential for a quick and cheap identification and classification of large numbers of potential chemical mutagens. The OECD QSAR Application Toolbox and Toxtree, which were developed for facilitating the practical use of (Q)SAR approaches in regulatory contexts, include two mechanistic SAR models for predicting the mutagenicity of aromatic amines and α-β unsaturated aliphatic aldehydes. The aim of this study was to estimate the interest and limitations of the former model. The model was first re-computed to check its transparency and to verify its statistical validity. Then, it was tested on about 150 chemicals not previously used for the design of the model but belonging to its domain of application. A critical analysis of the results was performed and proposals were made for increasing the model performances.

Mechanistic QSAR of aromatic amines: new models for discriminating between homocyclic mutagens and nonmutagens, and validation of models for carcinogens

Because of its environmental and industrial importance, the aromatic amines are the single chemical class most studied for its ability to induce mutations and cancer. The large database of mutagenicity and carcinogenicity results has been studied with Quantitative Structure-Activity Relationship (QSAR) approaches by several authors, leading to models for the following: (a) the mutagenic potency in Salmonella thyphimurium; (b) the carcinogenic potency in rodents; and (c) the discrimination between rodent carcinogens and noncarcinogens. However, satisfactory models for the discrimination between mutagens and nonmutagens are lacking. The present work provides new QSARs for mutagenic/nonmutagenic homocyclic aromatic amines in S. typhimurium strains TA98 and TA100. The two new models are validated by checking their ability to predict the mutagenicity of further aromatic amines not included in the training set, and not used to generate the QSAR models. In addition, we also validated previous QSAR models for the carcinogenicity/noncarcinogenicity of the aromatic amines with external data. The mechanistic implications of the models are discussed in light of the other QSARs for the aromatic amines. The results of the analysis point to two QSAR models (one for mutagenicity and one for rodent carcinogenicity) as reliable tools for the in silico characterization of the risk posed by the aromatic amines.

Synthesis and Decomposition of an Ester Derivative of the Procarcinogen and Promutagen, PhIP, 2-Amino-1-methyl-6-phenyl-1H-imidazo[4,5-b]pyridine: Unusual Nitrenium Ion Chemistry

The food-derived heterocyclic amine (HCA) carcinogen 2-amino-1-methyl-6-phenyl-1H-imidazo[4,5-b]pyridine, PhIP, is often generated in the highest concentration of the HCAs formed during broiling and frying of meat and fish. Although it is considered to be an important contributor to human cancer risk from exposure to HCAs, the chemistry of PhIP metabolites that presumably react with DNA to initiate carcinogenesis has received only cursory attention. We have synthesized the ester derivative N-pivaloxy-2-amino-1-methyl-6-phenyl-1H-imidazo[4,5-b]pyridine, 1b, and investigated its chemistry in aqueous solution. Although 1b was too unstable to isolate, we could characterize it by NMR methods in DMF-d7, a solvent in which it is stable at -40 degrees C. It decomposed rapidly in aqueous solution, but its conjugate acid, 1bH+, is not reactive. The nitrenium ion, 2, was trapped by N(3)(-) to form the unusual tetrazole adduct, 16. In the absence of N3-, the expected hydration products of 2 were not detected, but the reduction product, 12, was detected. Although such products are often taken as evidence of triplet nitrenium ions, the efficient trapping of 2 by N(3)(-) indicates that it is a ground state singlet species. The product 12 appears to be generated by reduction of an initially formed hydration product of 2. An alternative addition-elimination mechanism for the formation of 12 does not fit the available kinetic data. The selectivity of 2, measured as kaz/ks, the ratio of the second-order rate constant for its reaction with N(3)(-) and the first-order rate constant for its reaction with the aqueous solvent, is (2.3 +/- 0.6) x 10(4) M(-1), a value that is in the middle of the range of k(az)/k(s) of 10-10(6) M(-1) observed for nitrenium ions derived from other HCAs. The mutagenicity of aromatic amines (AAs) and HCAs, measured as the log of histidine revertants per nanomole of amine, log m, in Salmonella typhimurium TA 98 and TA 100 correlates with log(k(az)/k(s)) for a wide variety of carbocyclic and heterocyclic amine mutagens including PhIP. Previously developed linear regression models for mutagenicity that include log(k(az)/k(s)) as an independent variable predict log m for PhIP with good accuracy in both TA 98 and TA 100. Quantitative carcinogenicity data are less strongly correlated with log(k(az)/k(s)), so prediction of the carcinogenicity of PhIP and other HCAs or AAs based primarily on log(k(az)/k(s)) is less successful.

Tandem optimization of target activity and elimination of mutagenic potential in a potent series of N-aryl bicyclic hydantoin-based selective androgen receptor modulators

Pharmacokinetic studies in cynomolgus monkeys with a novel prototype selective androgen receptor modulator revealed trace amounts of an aniline fragment released through hydrolytic metabolism. This aniline fragment was determined to be mutagenic in an Ames assay. Subsequent concurrent optimization for target activity and avoidance of mutagenicity led to the identification of a pharmacologically superior clinical candidate without mutagenic potential.

Ultimate Carcinogenic Metabolites from Aromatic and Heterocyclic Aromatic Amines: A Computational Study in Relation to Their Mutagenic Potency

The formation of nitrenium ions from their precursors was examined by density functional theory (DFT) calculations in order to analyze the role of these electrophilic intermediates on the mutagenic activity of the parent amines. The relative reactivities for N-O bond dissociation from the N-hydroxy, N-acetoxy and N-sulfate derivatives of aniline were evaluated. Furthermore, the N-acetoxy esters from a set of 17 aromatic and heteroaromatic amines of diverse structure were considered, and correlations were sought between the calculated properties and the reported mutagenic potencies. The mutagenic activity was found to increase when a more negative charge developed at the exocyclic nitrogen of the nitrenium ion (qN) and with nitrenium ion stability. Different functional correlations were observed for the amine derivatives grouped according to their classification as aromatic (Ar), imidazo-carbocyclic (Imi-C), and imidazo-heterocyclic (Imi-H). The formation of N-acetyl nitrenium ions from aromatic amides was also considered and found to be less favorable than nitrenium ion generation from the corresponding amines.

A QSAR for the mutagenic potencies of twelve 2-amino-trimethylimidazopyridine isomers: structural, quantum chemical, and hydropathic factors

An isomeric series of heterocyclic amines related to one found in heated muscle meats was investigated for properties that predict their measured mutagenic potency. Eleven of the 12 possible 2-amino-trimethylimidazopyridine (TMIP) isomers were tested for mutagenic potency in the Ames/Salmonella test with bacterial strain TA98, and resulted in a 600-fold range in potency. Structural, quantum chemical, and hydropathic data were calculated on the parent molecules and the corresponding nitrenium ions of all of the tested isomers to establish models for predicting the potency of the unknown isomer. The principal determinants of higher mutagenic potency in these amines are: (1) a small dipole moment, (2) the combination of b-face ring fusion and N3-methyl group, (3) a lower calculated energy of the pi electron system, (4) a smaller energy gap between the amine HOMO and LUMO orbitals (Pearson "softness"), and (5) a more stable nitrenium ion. Based on predicted potency from the average of six regression models, the isomer not yet synthesized and tested is expected to have a mutagenic potency of 0.77 revertants/microg in tester strain TA98, which is near the low end of the potency range of the isomers.

Synthesis, cytotoxic activity on MCF-7 cell line and mutagenic activity of platinum(II) complexes with 2-substituted benzimidazole ligands

Four Pt(II) complexes with 2-H/or-methyl/or-aminomethylbenzimidazole or 1,2-dimethylbenzimidazole ligands as "non-leaving groups" were synthesized and their antiproliferative properties were tested against the human MCF-7 breast cancer cell line. The mutagenic potentials of the complexes were tested in Salmonella typhimurium strains TA 98 and TA 100 in the absence of S9 rat liver fraction. In general, Pt(II) complexes tested which were found to be less active than cisplatin, exhibited moderate in vitro cytotoxic activity on MCF-7 cell line. Among the complexes tested, Pt(II) complex with 2-aminomethylbenzimidazole ligand was found to be highly mutagenic in S. typhimurium TA 98 and low mutagenic in S. typhimurium TA 100. Pt(II) complex with 1,2-dimethylbenzimidazole was mutagenic only in S. typhimurium TA 98. The other two complexes were found to be non-mutagen in both strains.

Synthesis, characterization and in vitro cytotoxic, mutagenic and antimicrobial activity of platinum(II) complexes with substituted benzimidazole ligands

In this study, six Pt(II) complexes bearing 5(6)-H or -CH(3)-2-phenyl or -(2'-pyridyl) or -mercaptomethylbenzimidazole ligands as 'carrier groups' were synthesized and characterized by elemental analysis, IR and (1)H-NMR spectra and evaluated for their preliminary in vitro cytotoxic activity to the human RD Rhabdomyosarcoma cell line and mutagenic properties in Salmonella typhimurium strains TA 98 and TA 100 in the absence of the S9 rat liver fraction. The preliminary test results showed that the complexes had slightly greater cytotoxic activity on the RD cell line at 1 microM concentration than cisplatin. Among the compounds tested for their mutagenicity, Pt(II) complexes of 2-(2'-pyridyl)- and 5(6)-methyl-2-(2'-pyridyl)benzimidazoles were found to be mutagenic. A comparative study of the MIC (minimum inhibitory concentration) values indicated that, in general, there were no differences between the poor antimicrobial activity values of the ligands and their Pt(II) complexes with respect to the tested microorganisms. These results suggest that the synthesized Pt(II) complexes should be considered for further antitumor activity studies.

Correlations of nitrenium ion selectivities with quantitative mutagenicity and carcinogenicity of the corresponding amines

There is a correlation (r(adj)(2) = 0.5491-0.6338) of quantitative mutagenicity, log m, for a series of heterocyclic (HCAs) and carbocyclic (AAs) aromatic amines in Salmonella typhimurium TA 98 (18 amines) and TA 100 (15 amines) vs log S, the log of the azide/solvent selectivity of the corresponding nitrenium ion. Monocyclic aromatic amines, MAAs, are less mutagenic than other amines of similar log S. Multiple variable linear regression analysis led to a two parameter regression model, significant at the 95% confidence level for both variables, that includes log S and a ring index variable, I(rings), that is 0 for MAAs and 1 for all other amines. These models have r(adj)(2) = 0.8448 for TA 98, and 0.8927 for TA 100. Inclusion of a third variable, Clog P, increases r(adj)(2) to 0.8913 for TA 98 and 0.9011 for TA 100. This model is significant at the 95% confidence level for all variables for TA 98, but only for two of the three variables for TA 100. The confidence level is 80% for Clog P in TA 100. Quantitative carcinogenicity data in mice and rats are more weakly correlated with log S (r(adj)(2) = 0.5357 for 12 amines in mice, r(adj)(2) = 0.4216 for 10 amines in rats). Several two parameter regression models, all containing Clog P and one containing log S, adequately correlate the mouse data.

Computer-modeling-based QSARs for analyzing experimental data on biotransformation and toxicity

Over the past decades the description of quantitative structure-activity relationships (QSARs) has been undertaken in order to find predictive models and/or mechanistic explanations for chemical as well as biological activities. This includes QSAR studies in toxicology. In an approach beyond the classical QSAR approaches, attempts have been made to define parameters for the QSAR studies on the basis of quantum mechanical computer calculations. The conversion of relatively small xenobiotics within the active sites of biotransformation enzymes can be expected to follow the general rules of chemistry. This makes the description of QSARs on the basis of only one parameter, chosen on the basis of insight in the mechanism, feasible. In contrast, toxicological endpoints can very often be the result of more than one physico-chemical interaction of the compound with the model system of interest. Therefore the description of quantitative structure-toxicity relationships often does not follow a one-descriptor mechanistic approach but starts from the other end, describing QSARs by multi-parameter approaches. The present paper focuses on the possibilities and restrictions of using computer-based QSAR modeling for analyzing experimental toxicological data, with emphasis on examples from the field of biotransformation and toxicity.

Prediction of the genotoxicity of aromatic and heteroaromatic amines using electrotopological state indices

In the past decade, electrotopological state (E-state) indices have come into their own as useful descriptors for correlating a variety of physicochemical and biological properties of chemical compounds. Genotoxicity and mutagenicity, however, appear not to have been previously considered. In the present study, the genotoxicity of a set of 95 aromatic and heteroaromatic amines, which has been modeled previously using several sets of parameters, is modeled using E-state indices, both with and without principal components analysis. Parallels are drawn between E-state indices that were important in these models and other types of descriptors found significant in previous studies, thus, shedding light on connections to the molecular mechanism of activity. The best result had a correlation coefficient r = 0.876 and a standard error s< or = 1 log unit. These values are comparable to those in previously published models that were based on topological/geometric or on physicochemical parameters. They are not as good as those for a model based on descriptors derived from extensive quantum mechanical analysis, but E-state indices are much easier to compute.

Extended quantitative structure-activity relationships for 80 aromatic and heterocyclic amines: structural, electronic, and hydropathic factors affecting mutagenic potency

The mutagenic/carcinogenic heterocyclic amines formed during the cooking of protein foods have been determined to be probable or possible human carcinogens. As part of a comprehensive study of the food mutagens, our laboratory has produced a series of quantitative structure-activity relationships (QSARs) of aromatic and heterocyclic amines, to attempt to elucidate the mechanisms of mutagenesis/carcinogenesis. Amines are genotoxically active only after activation by a series of reactions converting the parent compound to an electrophilic derivative, which is postulated to be a nitrenium ion that covalently binds to and damages DNA. An important agent in this conversion is cytochrome P450. In this report we develop a QSAR for 80 amines of diverse structure and a range of 10 orders of magnitude in mutagenic potency. New structural factors and quantum chemical ab initio and Hückel calculations are included. The results are interpreted to show that a main determinant of mutagenic potency is the extent of the aromatic pi-electron system. Small contributions are made by both the dipole moment and the calculated stability of the nitrenium ion. Multiple linear regression models account for nearly two-thirds of the variance in potency, leaving room for additional unknown factors. The role of cytochrome P450 1A in amine toxification is supported, and further theoretical and experimental research on its reaction mechanisms and modeling of its active site are proposed.

Quantitative structure-activity relationship of flavonoids for inhibition of heterocyclic amine mutagenicity

The mutagenic/carcinogenic heterocyclic amines formed during the cooking of protein foods have been determined to be a potential risk to human health. Therefore, mitigation measures are beginning to be studied. A recent finding is that the induction of mutation in Salmonella by these amines can be inhibited by the addition of flavonoids to the assay. This study combines data on the inhibitory process with structural, ab initio quantum chemical, hydropathic, and antioxidant factors to develop a quantitative structure-activity relationship (QSAR) database and statistical analysis. For 39 diverse flavonoids the inhibitory potency varied approximately 100-fold. Three predictive variables, in order of decreasing contribution to variance, are: (1) a large dipole moment; (2) after geometric minimization of energy, a small departure from planarity (i.e., small dihedral angle between the benzopyran nucleus and the attached phenyl ring), and a low rotational energy barrier to achieving planarity; and (3) fewer hydroxyl groups on the phenyl ring. However, these variables account for less than half of the variance in inhibitory potency of the flavonoids. Frontier orbital energies and antioxidant or radical scavenging properties showed little or no relationship to potency. We conclude that interference by the flavonoids with cytochrome P450 activation of the promutagens is the probable mechanism for inhibition of mutagenesis, and suggest avenues for further research. Environ. Mol. Mutagen. 35:279-299, 2000 Published 2000 Wiley-Liss, Inc.

The alkaline single cell gel electrophoresis assay with mouse multiple organs: results with 30 aromatic amines evaluated by the IARC and U.S. NTP

The genotoxicity of 30 aromatic amines selected from IARC (International Agency for Research on Cancer) groups 1, 2A, 2B and 3 and from the U.S. NTP (National Toxicology Program) carcinogenicity database were evaluated using the alkaline single cell gel electrophoresis (SCG) (Comet) assay in mouse organs. We treated groups of four mice once orally at the maximum tolerated dose (MTD) and sampled stomach, colon, liver, kidney, bladder, lung, brain, and bone marrow 3, 8 and 24 h after treatment. For the 20 aromatic amines that are rodent carcinogens, the assay was positive in at least one organ, suggesting a high predictive ability for the assay. For most of the SCG-positive aromatic amines, the organs exhibiting increased levels of DNA damage were not necessarily the target organs for carcinogenicity. It was rare, in contrast, for the target organs not to show DNA damage. Organ-specific genotoxicity, therefore, is necessary but not sufficient for the prediction of organ-specific carcinogenicity. For the 10 non-carcinogenic aromatic amines (eight were Ames test-positive and two were Ames test-negative), the assay was negative in all organs studied. In the safety evaluation of chemicals, it is important to demonstrate that Ames test-positive agents are not genotoxic in vivo. Chemical carcinogens can be classified as genotoxic (Ames test-positive) and putative non-genotoxic (Ames test-negative) carcinogens. The alkaline SCG assay, which detects DNA lesions, is not suitable for identifying non-genotoxic carcinogens. The present SCG study revealed a high positive response ratio for rodent genotoxic carcinogens and a high negative response ratio for rodent genotoxic non-carcinogens. These results suggest that the alkaline SCG assay can be usefully used to evaluate the in vivo genotoxicity of chemicals in multiple organs, providing for a good assessment of potential carcinogenicity.

DNA adducts of heterocyclic amine food mutagens: implications for mutagenesis and carcinogenesis

The heterocyclic amines (HCAs) are a family of mutagenic/carcinogenic compounds produced during the pyrolysis of creatine, amino acids and proteins. The major subclass of HCAs found in the human diet comprise the aminoimidazoazaarenes (AIAs) 2-amino-3-methylimidazo[4,5-f]quinoline (IQ), 2-amino-3,4-dimethylimidazo[4,5-f]quinoline (MeIQ), 2-amino-3,8-dimethylimidazo[4,5-f]quinoxaline (MeIQx), 2-amino-3,4,8-trimethylimidazo[4,5-f]quinoxaline (DiMeIQx) and 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP). All, except DiMeIQx, have been shown to be carcinogenic in animals. These compounds are present in cooked muscle meats at the p.p.b. level. Since the discovery of the HCAs in the late 1970s, many studies have examined the DNA adducts of these compounds. This review compiles the literature on AIA-DNA adducts including their identification and characterization, pathways of formation, mutagenesis in vitro and in vivo, and their association with carcinogenesis in animal models. It is now known that metabolic activation leading to the formation of DNA adducts is critical for mutagenicity and carcinogenicity of these compounds. All of the AIAs studied adduct to the guanine base, the major adduct being formed at the C8 position. Two AIAs, IQ and MeIQx, also form minor adducts at the N2 position of guanine. A growing body of literature has reported on the mutation spectra induced by AIA-guanine adducts. Studies of animal tumors induced by AIAs have begun to relate AIA-DNA adduct-induced mutagenic events with the mutations found in critical genes associated with oncogenesis. Several studies have demonstrated the feasibility of chemoprevention of AIA tumorigenesis. Only a few studies have reported on the detection of AIA-DNA adducts in human tissues; difficulties persist in the routine detection of AIA-DNA adducts in humans for the purpose of biomonitoring of exposure to AIAs. The AIAs are nevertheless regarded as possible human carcinogens, and future research on AIA-DNA adducts is likely to help address the role of AIAs in human cancer.

Chemical and biological factors affecting mutagen potency

This review surveys the chemical and biological factors that are correlated with the mutagenic activity of the aromatic and heterocyclic amines. Particular attention is given to the predicted quantum chemical properties of the parent amines and their metabolites. A number of chemical properties have been found to correlate well with measured mutagenic potency, including log P, the energies of the frontier orbitals of the parent amines, and the thermodynamic stability of the nitrenium ion, possibly the ultimate DNA-binding species. These correlations are intriguing clues to the mutagenic activity of the aromatic amines; however, many factors still await final explanation, including the exact mechanisms of the metabolic enzymes, the identity(s) of the ultimate DNA-binding species, the reaction mechanism in the DNA-adduction, the role of sequence context in the covalent and non-covalent binding of the adducts, and the role of DNA repair.